Factorization machines (FM) are a set of algorithms that enhance the performance of linear models by incorporating second-order feature interactions that are absent in matrix factorization (MF) algorithms in a supervised way. Therefore, FMs are very robust compared to their classical counterpart—collaborative filtering (CF)—and are gaining popularity in personalization and recommendation systems because they can be used to discover latent features underlying the interactions between two different kinds of entities.
In this chapter, we will develop a sample project for predicting both the rating and ranking to show their effectiveness. Nevertheless, we will see some theoretical background of recommendation systems using MF and CF before diving into the project's implementation using RankSys...